Lung cancer is the top cancer killer and smoking remains the leading preventable cause of death in the US. Furthermore, major disparities in smoking and lung cancer exist by education, income, and race/ethnicity. While tobacco control policies are the most effective strategies to prevent lung cancers, lung cancer computed tomography (CT) screening has also been shown to reduce lung cancer risk among heavy current and former smokers. The Cancer Intervention and Surveillance Modeling Network (CISNET) lung group develops and applies population models for lung cancer, quantifying the impact of tobacco control and CT screening on lung cancer and all-cause mortality. To date, this work has focused on the country as a whole and has yet to account for tobacco and lung cancer disparities by subgroup and region. This proposed work will extend existing CISNET lung models to investigate the synergistic impacts of tobacco control policies and lung cancer screening in the US and in middle-income nations, focusing on disparities in both smoking behavior and lung cancer risk. The smoking and lung cancer models will incorporate other factors that reflect different smoking risks such as race/ethnicity, education, income, and geographic location. This will allow for analyses of the effects of tobacco control policies on US smoking prevalence in relevant high-risk groups, and estimation of the impact of policies on health disparities in smoking and lung cancer outcomes. Models will also be adapted for two middle-income nations, Mexico and Thailand, to evaluate the impact of tobacco control policies on smoking and lung cancer rates in middle-income countries with different demographic and smoking behavior profiles than the US. Although lung cancer screening is now supported by the USPSTF and other major national organizations, there is still considerable debate over the potential benefits and harms of screening at the population level. This project will extend CISNET lung cancer screening models to assess the consequences of heterogeneity in lung cancer screening implementation, insurance coverage, and compliance with recommended protocols on population health. Models will also be used to investigate the implications of using alternative individual risk prediction measures as eligibility criteria for screening, and to perfom cost-effectiveness analyses of current and alternative screening recommendations, with the goal of identifying optimal strategies at the population level.